Assessments and Workshops Help Institutions Determine Needs
AI workloads often have more complex infrastructure requirements than traditional applications, and different processes require different resources. For example, training and inference processes can be computing-intensive, while data-heavy use cases — such as research computing — demand scalable, high-performance storage.
There are a number of practical applications for using AI to enhance student success and back-office operations in higher ed, but institutions must ensure that their infrastructure can support these applications without compromising existing system performance. This means taking a close look at how storage and computing resources are provisioned across cloud and on-premises environments.
Assessments and planning workshops, such as those offered by CDW, can be great tools in helping IT teams understand their current environments; identify emerging AI workloads; and evaluate gaps in performance, capacity or cost visibility.
DISCOVER: Three questions to ask before beginning an infrastructure modernization project.
Taking a holistic look at an institution’s infrastructure can help IT leaders identify opportunities to optimize existing investments rather than defaulting to making costly new purchases. This approach also helps institutions consider which workloads truly require advanced computing capabilities and which can run on standard infrastructure.
Evaluating Usage and Spending Can Optimize Costs
On the surface, it might seem like adding capacity is the easy solution for infrastructure optimization, but that’s not always the case. The location of storage is just as important as the amount available. Institutions often choose to segment storage based on data type, performance requirements and compliance needs, as some workloads are better suited for on-premises environments, public cloud platforms or hybrid models.
Visibility into usage and cost is especially important as AI workloads scale. Monitoring tools and cloud management platforms can help institutions understand consumption patterns and identify optimization opportunities before costs escalate.
Many higher education institutions operate under long-term agreements with cloud providers. While these agreements can feel limiting, they often include opportunities to modernize through cloud marketplaces and committed spending programs. Often these marketplaces include software and services that institutions need in order to support AI initiatives, so using existing credits to purchase these can help manage costs and reduce procurement complexity.
Aligning Infrastructure With AI Readiness and Institutional Goals
To minimize disruption to campus operations and test the waters of AI use, many institutions are introducing pilot projects rather than full-scale AI initiatives. These pilots may involve a small number of classrooms, a specific administrative process or a targeted student service. By testing projects on a smaller scale, IT teams can evaluate infrastructure configurations, gather feedback and refine support models before expanding these initiatives campuswide. Pilot-based approaches help institutions build confidence and internal expertise while reducing the risk associated with large-scale deployments.
READ MORE: Is your infrastructure AI-ready?
Ultimately, storage and computing decisions should be driven by clearly defined AI goals rather than technology trends. Institutions that align infrastructure planning with governance frameworks, prioritized use cases and measurable outcomes are better positioned to scale AI responsibly. By working with a trusted third party and taking a phased, intentional approach to achieve goals that support an institution’s mission, colleges and universities can prepare their infrastructure for AI without compromising performance.
